In the United States (US), an estimated 20 million adults have been diagnosed with substance use disorder (SUD). A major challenge for patients is to identify the most appropriate treatment with the best outcomes. Patients receive care in outpatient and inpatient facilities yet quality metrics at the level of these individual facilities are not publicly available. In the era of digital data, the Internet and peer-to-peer resources are often the first place where individuals look to find information about healthcare resources. Online reviews on sites like Google and Yelp provide narratives about healthcare facilities and assign easily interpretable star ratings ranging from one to five stars. These reviews of healthcare facilities provide information about patient experience, structure (e.g. physical education) facility, organizational characteristics, payment methods), process (e.g. diagnosis, treatment, patient and outcomes (e.g. knowledge, health-related quality of life morbidity, mortality). Prior work has demonstrated that online ratings of hospitals correlate with ratings from the national Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey. Little work however has evaluated the utility of unvalidated, spontaneously generated, but widely accessible online reviews of SUD treatment facilities. These reviews could fill a niche with providing patients and their family members with useful information about the questions and experiences that are most important to them. Or these reviews could be limited and provide misinformation or only biased perspectives. To date, less is known about the potential value or usefulness of this emerging data source. For this proposal we aim to study online reviews of SUD treatment facilities in the US to identify the areas of care that are reported as most important to patients and family members. For Aim 1, we will first extract approximately 50,000 online reviews of SUD treatment facilities and code them for themes using qualitative methodologies that involve manual coding and big data analytics using machine learning and natural language processing. We hypothesize that these online narratives will include qualitative data about patient experience and the type and quality of care (e.g. evidence-based treatments) provided at SUD treatment facilities. For Aim 2, we will then assess how star ratings differentiate facilities relative to structure and process measures reported in the National Survey of Substance Abuse Treatment Services. Overall, emerging online data resources have the potential to provide new information about a critically vulnerable population affected by the opioid and more broadly the SUD crisis. Our project seeks to rigorously study these new patient-centric data sources viewed by millions of individuals for the purposes of better understanding the needs of patients and family members. These efforts can lay the groundwork for future work in developing measures of quality for a critically important healthcare resource, SUD treatment facilities.